Dynamic Optimization of Railcar Traffic Volumes at Railway Nodes

  • Aleksandr RakhmangulovEmail author
  • Aleksander Sładkowski
  • Nikita Osintsev
  • Pavel Mishkurov
  • Dmitri Muravev
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 87)


A major direction in the development of modern world transport systems involves the concentration of freight and traffic flows within international transport corridors and transport nodes in terminals and hubs. The changing role of rail transport is taking place under these conditions. Increased structural complexity and irregularities in cargo and railcar traffic volumes have been observed, despite the higher levels of transport equipment and technology standardization, the increased container transport volumes and consequent reduction in the cost of intermodal operations, and the interaction between different modes in transport nodes. This is largely due to the increasing need for cargo owners to lower logistics and warehouse costs, which is achieved by reducing the size of freight shipments and ensuring their uniform delivery. Moreover, privatization of the railway industry in certain countries and the sale of rolling stock to operating companies have made the coordinated management of rail fleets more difficult. The demand for improved efficiency of railcar traffic volume management in the case of complex structures is especially relevant for large railway nodes, particularly the transport systems of industrial enterprises. Here, the application of traditional approaches to the management of transportation processes involving individual elements of traffic flow (trains, railcars, locomotives) and transport infrastructure (railway stations, loading areas, rail hauls) leads to additional transport costs as a result of the increased length of time that railcars are located at the railway node. The aim of this study, therefore, is to provide improved methods for the management of RTVs at complex railway nodes based on a systematic review of RTVs in conjunction with transport infrastructure and traffic control systems. The authors review the case of a systematic approach to the organization and management of railcar traffic volumes, both for mainline rail transport and at railway nodes and industrial rail transport. This study investigates the impact of irregular railcar traffic volumes on railway node functioning by applying a dynamic simulation model of the transport system of a large metallurgical enterprise. The application of the original methodology for assessing the amount of information in the operational management system for rail transportation allowed us to estimate the effect of structural complexity and railcar traffic volume irregularities on the efficiency of the dispatch control system. The authors propose a new system of parameters and indicators for assessing railcar traffic volumes, taking into account factors of complexity and irregularity, and provide a comprehensive assessment of its effectiveness for railcar traffic volume management. For this purpose, a method of dynamic optimization of these parameters (dynamic programming) is selected. The main hypothesis of the study is that improved accuracy in parameter optimization for irregular railcar traffic volumes is achieved by adjusting the duration of the base periods which constitute the optimization period in the dynamic problem. In this study we have formulated a mathematical model for dynamic parameter optimization of railcar traffic volume on the basis of base periods of variable duration, with an algorithm for model implementation as well. Experimental verification of the effectiveness of the developed model and algorithm is conducted on the constructed process-centric railway node simulation model. Three series of experiments are conducted: without railcar traffic volume optimization, and with dynamic optimization of railcar traffic volume with the application of base periods of constant and variable duration. The experimental results demonstrate increased optimization accuracy with the use of the proposed model, reducing transport costs (time of railcar traffic volume handling) at the railway node by 11% on average. For the realization of the model and algorithm, a method is proposed for their integration in information management and intellectual transport systems.


Railway node Railcar traffic volumes Irregularity Railcar traffic volumes with complex structure Information quantity System of parameters for railcar traffic volumes System dynamic model Dynamic optimization Mathematical model Process-centric simulation model 


  1. 1.
    Ostrovskiy AM, Druzhinina MG, Kuzmin AA (2011) Interaction of operator companies with industry and railroad. Rail Transp (2):61–63 (in Russian)Google Scholar
  2. 2.
    Rakhmangulov AN, Mishkurov PN, Kopylova OA (2014) Railway transport technological systems: organization of functioning: monograph. Magnitogorsk, Nosov Magnitogorsk State Technical University (in Russian) Google Scholar
  3. 3.
    Okulov NE (2014) Methods and ways for improvement the interaction of production and transport. PhD thesis, Ural State University of Railway Transport (in Russian)Google Scholar
  4. 4.
    Marfin MA, Kozlov PA, Bugaev AV (1986) Simulation model increased the throughput. Ind Transp (12):8–9 (in Russian)Google Scholar
  5. 5.
    Levin DY (1988) Optimization of train flows. Transport, Moscow (in Russian)Google Scholar
  6. 6.
    Rezer SM (1982) Integrated management of transportation process at transport nodes. Transport, Moscow (in Russian)Google Scholar
  7. 7.
    Persianov VA (1983) Stations and nodes in modern transport system. Rail Trans (3) (in Russian)Google Scholar
  8. 8.
    Sotnikov IB (1967) Theoretical foundations of interaction in the work of train departure parks of stations and surrounding areas. Moscow State University of Railway Engineering (in Russian)Google Scholar
  9. 9.
    Clausen U, Rotmann M (2014) Measurement and optimization of delivery performance in industrial railway systems. In: Efficiency and innovation in logistics. Lecture Notes in Logistics, pp 109–120Google Scholar
  10. 10.
    Clausen U, Voll R (2013) A comparison of North American and European railway systems. Euro Transp Res Rev 5(3):129–133CrossRefGoogle Scholar
  11. 11.
    Rakhmangulov AN, Trofimov SV, Kornilov SN (2004) Methods for development the systems of industrial rail transport in a changing environment activities of the enterprises, Magnitogorsk. Nosov Magnitogorsk State Technical University (in Russian)Google Scholar
  12. 12.
    Trofimov SV (2004) Scientific-methodical bases of functioning and development of industrial transport systems. Doctoral dissertation, Moscow State University of Railway Engineering (in Russian)Google Scholar
  13. 13.
    Osintsev NA, Rakhmangulov AN (2013) Railcar traffic volumes management in industrial transport systems. Vestnik of Nosov Magnitogorsk State Techn Univ 1(41):16–20 (in Russian)Google Scholar
  14. 14.
    Kozlov PA (1988) Theoretical basis, organizational forms, methods of optimization for flexible transport services of black metallurgy factories. Doctoral dissertation, Moscow (in Russian)Google Scholar
  15. 15.
    Rakhmangulov AN, Osintsev NA, Mishkurov PN, Kopylova OA (2014) Intellectualization of transport service of the metallurgical enterprises. Steel (4):115–118 (in Russian)Google Scholar
  16. 16.
    Popov AT (1984) Optimization of interaction process for railway transport and production (on example of steel plant). Candidate dissertation, Moscow (in Russian)Google Scholar
  17. 17.
    Komarov AV (ed) (1983) Problems of transport development in USSR. Comprehensive operation. Transport, Moscow (in Russian)Google Scholar
  18. 18.
    Geraets F, Kroon L, Schoebel A, Wagner ZC (eds) (2004) Algorithmic methods for railway optimization, 320 p (in Russian)Google Scholar
  19. 19.
    Reggiani A, Schintler LA (2005) Methods and models in transport and telecommunications cross Atlantic perspectives. Springer, Berlin 364 pCrossRefGoogle Scholar
  20. 20.
    Cascetta E (2009) Transportations systems analysis: models and applications. Springer, Berlin, 681 p (in Russian)Google Scholar
  21. 21.
    Aleksandrov AE (1995) Flexible control technology of inroad loop routes. Candidate dissertation, Moscow (in Russian)Google Scholar
  22. 22.
    Baturin AP, Borodin AF, Panin VV (2010) Railcar traffic volumes organization into same group of trains. World Transp 5(33):72–77 (in Russian)Google Scholar
  23. 23.
    Osminin AT (2000) Rational organization of railcar traffic volumes based on the methods of multi-criteria optimization. Doctoral dissertation, Samara (in Russian)Google Scholar
  24. 24.
    Bodyul VI (2006) Improving the rhythm and efficiency of transport production through reduction of daily irregularity of freight traffic on the railways. Doctoral dissertation, Moscow (in Russian)Google Scholar
  25. 25.
    Aleksandrov AE, Yakushev NV (2006) Stochastic formulation of the dynamic transport problem with delays and the random spread of delivery time and time consumption. Manag Big Syst (12–13):5–14 (in Russian)Google Scholar
  26. 26.
    Kozlov PA, Vladimirskaya IP (2009) Systems construction of automatic control for flows of railcars of different owners. Vestnik Railway Res Inst (6):8–11 (in Russian)Google Scholar
  27. 27.
    Rakhmangulov AN, Mishkurov PN (2012) Problems of method application of dynamic programming for operational management for railcar traffic volume. Mod Prob Russ Transp Complex (2):279–285 (in Russian)Google Scholar
  28. 28.
    Carey M, Lockwood D (1995) Model, algorithms and strategy for train pathing. J Oper Res Soc 46(8):988–1005CrossRefzbMATHGoogle Scholar
  29. 29.
    Carey M (1994) A model and strategy for train pathing with choice of lines, platforms, and routes. Transp Res Part B 28(5):333–353CrossRefGoogle Scholar
  30. 30.
    Carey M (1994) Extending a train pathing model from one-way to two-way track. Transp Res Part B 28(5):395–400CrossRefGoogle Scholar
  31. 31.
    Dorfman MJ, Medanic J (2004) Scheduling trains on a railway network using a discrete event model of railway traffic. Transp Res Part B: Methodol 38(1):81–98CrossRefGoogle Scholar
  32. 32.
    Rakhmangulov A, Kolga A, Osintsev N, Stolpovskikh I, Sładkowski A (2014) Mathematical model of optimal empty rail car distribution at railway transport nodes. Transp Prob 9(3):125–132Google Scholar
  33. 33.
    Caprara A, Kroon LG, Monaci M, Peeters M, Toth P (2007) Passenger railway optimization. In: Barnhart C, Laporte G (eds) Handbooks in operations research and management science, vol 14, pp 129–187Google Scholar
  34. 34.
    Caimi G, Chudak F, Fuchsberger M, Laumanns M, Zenklusen R (2011) A new resource-constrained multicommodity flow model for conflict-free train routing and scheduling. Transp Sci 45(2):212–227CrossRefGoogle Scholar
  35. 35.
    Blum J, Eskandarian A (2002) Enhancing intelligent agent collaboration for flow optimization of railroad traffic. Transp Res Part A: Policy Pract 36(10):919–930CrossRefGoogle Scholar
  36. 36.
    Teornquist J (2007) Railway traffic disturbance management an experimental analysis of disturbance complexity, management objectives and borderations in planning horizon. Transp Res A: Policy Pract 41(3):249–266Google Scholar
  37. 37.
    Carey M, Crawford I (2007) Scheduling trains on a network of busy complex stations. Transp Res B: Methodol 41(2):159–178CrossRefGoogle Scholar
  38. 38.
    Erlebach T, Gantenbein M, Heurlimann D, et al. (2001) On the complexity of train assignment problems. In: Algorithms and computation, 12th international symposium, ISAAC 2001 Christchurch, New Zealand, Proceedings, vol 2223 of Lecture Notes in Computer Science, pp 390–402Google Scholar
  39. 39.
    Meng X, Jia L, Chen C, Xu J, Wang L, Xie J (2010) Paths generating in emergency on China new railway network. J Beijing Inst Technol 19(2):84–88Google Scholar
  40. 40.
    Lee Y, Chen C (2009) A heuristic for the train pathing and timetabling problem. Transp Res B 43(8–9):837–851CrossRefGoogle Scholar
  41. 41.
    Pellegrini P, Marli`ere G, Rodriguez J, Marliere G (2014) Optimal train routing and scheduling for managing traffic perturbations in complex junctions. Transp Res Part B 59:58–80Google Scholar
  42. 42.
    Lusby RM, Larsen J, Ehrgott M, Ryan DM (2013) A setpacking inspired method for real-time junction train routing. Comput Oper Res 40(3):713–724CrossRefzbMATHGoogle Scholar
  43. 43.
    D’Ariano A (2008) Improving real-time train dispatching: models, algorithms and applications. PhD thesis, Delft University of Technology, Delft, The NetherlandsGoogle Scholar
  44. 44.
    Goverde RMP, Hansen IA (2000) TNV-prepare: analysis of Dutch railway operations based on train detection data. In: Allan J, Brebbia CA, Hill RJ, Sciutto G, Sone S (eds) Computers in railways VII. WIT Press, Southampton, UK, pp 779–788Google Scholar
  45. 45.
    Goverde RMP (2005) Punctuality of railway operations and timetable stability analysis. PhD thesis, Delft University of Technology, Delft, The NetherlandsGoogle Scholar
  46. 46.
    Fugenschuh A, Homfeld H & Schulldorf H (2009) Single car routing in rail freight transport. In: Barnhart C, Clausen U, Lauther U, Mohring R (eds) Dagstuhl seminar proceedings 09261, Schloss Dagstuhl - Leibniz-Zentrum fr Informatik, DeutschlandGoogle Scholar
  47. 47.
    Fugenschuh A, Homfeld H, Huck A, Martin A, Yuan Z (2008) Scheduling locomotives and car transfers in freight transport. Transp Sci 42(4):1–14CrossRefGoogle Scholar
  48. 48.
    Barnhart C, Jin H, Vance PH (2000) Railroad blocking: a network design application. Oper Res 48(4):603–614CrossRefGoogle Scholar
  49. 49.
    Jha KC, Ahuja RK, Sahin G (2007) New approaches for solving the block-to-train assignment problem. Networks 51(1):48–62MathSciNetCrossRefzbMATHGoogle Scholar
  50. 50.
    Ahuja RK, Jha KC, Liu J (2007) Solving real-life railroad blocking problems. Interfaces 37(5):404–419CrossRefGoogle Scholar
  51. 51.
    Hailes S (2006) Modern telecommunications systems for train control. In: The 11th institution of engineering and technology professional development course on railway signalling and control systems, Manchester, UK, pp 185–192Google Scholar
  52. 52.
    Kauppi A, Wikström J, Sandblad B, Andersson AW. Future train traffic control: control by re-planning. Cogn Technol Work 8(1):50–56. Available at:
  53. 53.
    Muravev DS, Rakhmangulov AN, Mishkurov PN (2013) Application of simulation modeling to evaluate handling capacity of sea ports and justification of the need for dry port construction. Mod Probl Russian Transp Complex 4(4):66–72 (in Russian)Google Scholar
  54. 54.
    Turanov HT, Chuev NP (2012) Construction of differential models of the rolling stock movement on non-public places. Transp Sci Technol Manag 7:13–18 (in Russian)Google Scholar
  55. 55.
    Turanov HT, Chuev NP, Portnova OU (2013) Mathematical modeling of freight railcars movement on driveway tracks of the enterprise. Sci Technol Transp (1):26–42 (in Russian)Google Scholar
  56. 56.
    Turanov HT, Chuev NP, Portnova OU (2013) Numerical simulation of the freight ralcars movement on driveway tracks of industrial enterprises in maple. Transp Sci Technol Manag (12):7–14 (in Russian)Google Scholar
  57. 57.
    Rakhmangulov AN, Kolga AD, Osintsev NA, Stolpovskikh IN, Sładkowski AV (2014) Mathematical model of optimal empty rail car distribution at railway transport nodes. Transp Prob 9(3):125–132Google Scholar
  58. 58.
    Hernando A, Roanes-Lozano E, Maestre-Martínez R, Tejedor J (2010) A logic-algebraic approach to decision taking in a railway interlocking system. Ann Math Artif Intell 65(4):317–328MathSciNetCrossRefzbMATHGoogle Scholar
  59. 59.
    Corman D, D’Ariano A, Pacciarelli D, Pranzo D (2010) Railway dynamic traffic management in complex and densely used networks intelligent infrastructures. Intell Syst Control Autom: Sci Eng 42:377–404 (in Russian)Google Scholar
  60. 60.
    White TA (2007) The development and use of dynamic traffic management simulations in North America. In: Hansen IA, Radtke A, Pachl J, Wendler E (eds) Proceedings of the 2nd international seminar on railway operations modelling and analysis, Hannover, GermanyGoogle Scholar
  61. 61.
    Sotnikov EA, Poplavskiy AA (2007) General principles of system construction for operational management of the transportation process. Transp Sci Technol Manag (2):80–87 (in Russian)Google Scholar
  62. 62.
    Kozlov PA, Vladimirskaya IP (2009) A method for optimizing the structure of transport system. World Transp 26(2):84–87 (in Russian)Google Scholar
  63. 63.
    Kozlov PA (2007) Information technologies in transport. The modern stage. Transp Russ Fed (10):38–41 (in Russian)Google Scholar
  64. 64.
    Kozlov PA, Vladimirskaya IP (2009) Optimization method of interaction in the production and transport systems. Modern Prob Sci Educ (2–6):17–18 (in Russian)Google Scholar
  65. 65.
    Aleksandrov AE (2008) The application of models within the calculation and optimization of systems for rail transport. Sci Technol Transp (2):54–56 (in Russian) Google Scholar
  66. 66.
    Kutirkin AV (2004) Development of models and algorithms for solving functional tasks of the management for transport systems and production. Doctoral dissertation, Moscow (in Russian)Google Scholar
  67. 67.
    Potzsche C, Heuberger C, Kaltenbacher B, Rendl F (eds) System modeling and optimization (2014) 26th IFIP TC 7 conference, CSMO 2013, Klagenfurt, Austria. Springer, Berlin, 359 p (in Russian)Google Scholar
  68. 68.
    Kozlov PA, Osokin OV, Permikin VU (2013) Automated control of the processes in the transport node. Vestnik Rostov State Univ Railway Eng 2(50):118–122 (in Russian)Google Scholar
  69. 69.
    Borshchev A (2013) The big book of simulation modeling: multimethod modeling with AnyLogic 6. AnyLogic, North AmericaGoogle Scholar
  70. 70.
    Mishkurov PN (2012) Typification of industrial railway stations. Mod Prob Russ Transp Complex. (2):143–151 (in Russian)Google Scholar
  71. 71.
    Gromov NN, Persianov VA, Uskov NS (2003) Management on transport. Academy, Moscow (in Russian)Google Scholar
  72. 72.
    Gavrishev SE, Dudkin EP, Kornilov SN, Rakhmangulov AN, Trofimov SV (2003) Transport logistics. St. Petersburg (in Russian)Google Scholar
  73. 73.
    Kaigorodcev AA, Rakhmangulov AN (2013) Factors of efficiency for logistics distribution centers. Vestnik transporta Povolzhya 2:11–19 (in Russian)Google Scholar
  74. 74.
    Booch G, Maksimchuk R, Michael W, Bobbi Y, Jim C, Houston K (2008) Object-oriented analysis and design with applications. E-Book, 720 pGoogle Scholar
  75. 75.
    Pepevnik A, Belsak M (2011) Information system in the function of railway traffic management. Transp Prob 6(1):37–42Google Scholar
  76. 76.
    Treiber M, Kesting A (2013) traffic flow dynamics. Data, models and simulation. Springer, Berlin, p 506CrossRefzbMATHGoogle Scholar
  77. 77.
    Ran B, Boyce D (1996) Modeling dynamic transportation networks. An intelligent transportation system oriented approach. Springer, Berlin 356 pCrossRefzbMATHGoogle Scholar
  78. 78.
    Rakhmangulov AN, Mishkurov PN (2012) Features of a simulation model building for technology of the railway station in the AnyLogic system. Collect Sci Works SWorld 2(4):7–13 (in Russian)Google Scholar
  79. 79.
    Sładkowski A, Pamuła T (eds) (2016) Intelligent transportation systems—problems and perspectives, Studies in Systems Decision and Control, vol 32. Springer, Switzerland, 303 pGoogle Scholar
  80. 80.
    Muravev D, Aksoy S, Rakhmangulov A, Aydogdu V (2016) Comparing model development in discrete event simulation on Ro-Ro terminal example. Int J Logistics Syst Manag 3(24):283–297Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Aleksandr Rakhmangulov
    • 1
    Email author
  • Aleksander Sładkowski
    • 2
  • Nikita Osintsev
    • 1
  • Pavel Mishkurov
    • 1
  • Dmitri Muravev
    • 1
  1. 1.Nosov Magnitogorsk State Technical UniversityMagnitogorskRussia
  2. 2.Silesian University of TechnologyKatowicePoland

Personalised recommendations